The modelling and predictive technologies department applies state-of-the-art protein modelling and data science technologies to design next generation medicines that will change the life of patients with diabetes and other serious chronic diseases.
In the department we collaborate closely with research scientists across the R&D area to improve and accelerate active pharmaceutical ingredient (API) and drug product (DP) designs of new peptides and biologics. We have access to advanced state-of-the-art protein modelling software and are developing and applying data science tools and prediction models to a variety of biological and chemical data, including high-throughput analytical data and images.
One aspect of our work is using machine learning and deep-learning AI technology to automate semi-complex human cognitive tasks. This will supplement human analysis, allowing users to dedicate more time to analysing complex and non-standard conditions. More powerful automation means faster and more reliable drug creation.
There are 3 main groups within our department. Our modelling scientists examine and model molecules and proteins in order to create better and more stable drugs. Our data scientists (with physics or computer science background) understand and apply complex machine learning algorithms. Our data engineers ensure that our entire working infrastructure is as agile and stable as possible.
On a personal level, our data scientists must be outgoing and personable. With strong collaborative and communication skills, we work with fellow scientists to identify barriers and challenges in our daily work. Data scientists in our department have technical backgrounds in computational life science disciplines, physics or computer science, and typically have a PhD in their field. Expert programming skills in languages like Python and R are crucial for all members of our team.
The Structure, Bioinformatics and Data Science functional area is part of Global Research Technologies R&D and is focused on Structure & Computational Modelling, Scientific Research Software Applications, Predictive Technologies, and Bioinformatics. By enabling full utilization of our scientific data, our aspiration is to create truly innovative medicines for patients. Listen as three members of the area discuss their work, colleagues, and ambitions.
Jesper is a principal scientist within the modelling and predictive technologies department. Since joining Novo Nordisk in 2016, Jesper has worked on applying machine learning, biophysics, and AI tools to enable the automation of data analysis. Jesper talks about the department, their crucial work, and the overall vision of creating better medicines for our patients.